Paper authored by KU Team proposes new ‘log-lifting’ framework to make business process models more accurate and valuable

A paper written by a team of researchers from Khalifa University that offers a new process mining solution that helps companies develop more accurate and insightful business process models was nominated for the Best Student Paper Award at the 17^th International Conference on Business Processing Management 2019, which took place earlier this month in Vienna.

The paper, which was one of three to be nominated for the selective award, was authored by PhD student Ghalia Tello, Dr. Gabriele Gianini, Senior Researcher at the Emirates ICT Innovation Center (EBTIC), Dr. Rabeb Mizouni, Associate Professor of Computer Engineering, and Professor Ernesto Damiani, Senior Director of the Artificial Intelligence and Intelligent Systems Institute and Director of the Center for Cyber-Physical Systems (C2PS).

Process mining techniques analyze business process activity data from different perspectives and summarize them into useful information for making business decisions. Many businesses have an IT system that stores data in databases – such as patient treatment records, student data, or order handling – and creates ‘event logs’ with that data. Process mining uses those event logs to develop a process model that helps to visualize and analyze the real-life execution of the company’s processes.

Examples of business processes are the process of handling a customer order, a job application, an insurance claim, a building permit, a leave permit or handling a patient in an emergency room.

An activity is a well-defined step in the process, for instance handling a patient in an emergency room involves patient registration, triage (i.e. assigning a priority to the patient based on the seriousness of his/her medical condition) and so on.

Process mining techniques can deliver valuable, factual insights into how processes are being executed in real life. Mining a process can help to discover anomalies/violations that occurred in the process, or even to predict probable future anomalies based on past records. It can also support process optimization in terms of effectiveness.

Unfortunately, real-life processes tend to be more complex and less structured than most process mining algorithms are designed to handle. A major challenge that occurs with process mining is that one cannot normally observe the process activities directly, but only through the recorded event logs (e.g. the patient triage may involve an initial observational assessment, heart auscultation, blood pressure measurement, the transcription of an account of the symptoms). Often the events recorded in the event log are too fine-grained. “This can cause the algorithms designed to discover processes to not accurately represent the process at the right level of abstraction,” said Tello. “And get lost in details.”

Tello’s paper proposes a ‘log-lifting’ framework method that uses machine learning to abstract the event log to a lower level of granularity, thus bridging the abstraction level gap between the logs and the activities meaningful for the process model. Abstraction methods provide a mapping from the recorded events to activities recognizable by process workers.

The log-lifting framework proposed by the paper comprises two main phases: event log segmentation and machine-learning-based classification.

“The purpose of the segmentation phase is to identify the potential segment separators in a flow of low-level events, in which each segment corresponds to an unknown high-level activity,” Tello explained. “For this, we proposed a segmentation algorithm based on maximum likelihood with n-gram analysis – a standard technique used to model statistical regularities in languages, or any sequence of elements such as letters or words.”

In the second phase, event segments were mapped into their corresponding high-level activities using a machine learning classification methods. The KU team explored different classification methods, including Artificial Neural Network (ANN) and Random Forest algorithms.

The method was evaluated in collaboration with the German multinational software company SAP, using an event log from their NetweaverLog system. The evaluation showed that the team’s log-lifting framework provides an accurate representation of the process at the right level of abstraction – the activity level.

The KU team aims to build on their research to develop an end-to-end process mining framework that incorporates further log-lifting techniques and improve the capabilities of the system to detect and predict process anomalies, with the final goal of providing any business process endowed with logs with the capability of improving its effectiveness thus increasing its business value.

Erica Solomon
Senior Editor
26 September 2019

The post Process Mining Paper Nominated for Best Student Paper Award at International Conference in Vienna appeared first on Khalifa University.

PhD student Lilas Alrahis won Best Paper Award for her paper titled “ScanSAT: Unlocking Obfuscated Scan Chains,” at the Applied Research Competition – MENA Region, one of nine competitions held in association with the New York University’s Cyber Security Awareness Week (CSAW) 2019, which took place from 6-8 November 2019.

Her winning paper addresses a key security issue arising out of the shifting microelectronics supply chain landscape.

“The dramatic increase in fabrication costs of Integrated Circuits (ICs) has led to the globalization of the IC supply chain, raising concerns regarding IC piracy, reverse engineering, and hardware Trojan insertion. ICs are the heart of electronic systems that are embedded in a wide range of applications. Therefore, ensuring trust in the IC supply chain is vital in order to guarantee a reliable and trustworthy platform to build on,” Alrahis explained.

The IC supply chain is witnessing the outsourcing of key steps, such as testing, to Outsourced Semiconductor Assembly and Test (OSAT) companies, which may damage or compromise on-chip assets. To prevent piracy and inappropriate use of ICs, chip makers are incorporating hardware locking systems as an important aspect of chip design.

One technique, called “obfuscation of scan chains,” hides the functionality of the chip design from the untrusted testers via the insertion of additional logic elements. The method involves inserting a type of logic between the chip elements that shift chip test data in and out of the chip to make every point in the chip controllable and observable. The logic is driven by a secret key to hide the transformation functions between the inputs, outputs and captured test data responses.

Alrahis leverages this technique and proposes the use of ScanSAT, a type of attack that transforms a scan obfuscated circuit to its logic-locked version and applies the Boolean satisfiability (SAT) based attack, which allows the user to extract the secret key.

The research work was carried out under KU’s System-on-Chip Lab (SoCL) with supervisors Dr. Hani Saleh, Associate Professor of Electrical Engineering and Computer Science, Dr. Baker Mohammad, Associate Professor of Electrical Engineering and Computer Science, and Dr. Mahmoud Al Qutayri, Professor of Electrical Engineering and Computer Science, in collaboration with Dr. Ozgur Sinanoglu, Professor of Electrical and Computer Engineering from NYUAD.

CSAW is the most comprehensive student-run cyber security event in the world, featuring nine competitions across six global regions. In the Applied Research Competition, industry experts served as judges who evaluated the originality, relevance, and accuracy of the research.

The achievement is a testament to the research and development being carried out at KU that aims to address the rapidly emerging changes in the landscape for cybersecurity.

“Participating in CSAW’19 was a great opportunity to present my research work and utilize the skills I have gained during my PhD. The experienced judges gave critical and valuable feedback on my work. Also, winning the competition provides proof of our important research work and distinguishes our publication from the rest,” Alrahis shared.

Erica Solomon
Senior Editor
18 November 2019

The post Student Wins CSAW Best Paper Award for Hardware Locking System to Protect ICs appeared first on Khalifa University.